Intelligent monitoring method and equipment for macromolecular damping wall and medium

Abstract

The invention discloses an intelligent monitoring method and device for a polymer damping wall and a medium, and relates to the field of damping wall monitoring, and the method comprises the steps: collecting the monitoring data of a target damping wall through a pre-deployed sensor device, and transmitting the monitoring data to a local storage in real time; preprocessing the monitoring data, and based on the preprocessed monitoring data, performing vibration characteristic analysis on the target damping wall to obtain vibration characteristics; based on the vibration characteristics, through a machine learning algorithm, carrying out abnormity identification on the vibration mode of the target damping wall; and determining whether the target damping wall has abnormal vibration or not according to the identification result. By deploying sensor equipment, vibration data of the wall body are collected in real time and transmitted to local storage, and continuous monitoring of vibration of the wall body is achieved. The vibration data is automatically collected through the pre-deployed sensor, and the abnormity identification is automatically carried out through the machine learning algorithm, so that the artificial error is avoided, and the accuracy of data acquisition can be ensured.

Description

Technical Field

[0001] This application relates to the technical field of damping wall monitoring, and particularly to an intelligent monitoring method, device, and medium for a polymer damping wall. Background Art

[0002] Polymer damping materials are polymer materials with functions such as sound absorption, heat insulation, and vibration prevention. They can convert mechanical vibration energy or sound energy into heat energy and dissipate it, thereby preventing or reducing the damage of mechanical vibration to components. They can be used to make sound insulation and seismic devices for buildings or mechanical equipment, thereby reducing noise transmission and improving stability and safety.

[0003] A polymer damping wall is a shock-absorbing device made of polymer materials. Its principle is that the steel plate moves in a highly viscous damping liquid made of polymer materials, causing the damping liquid to produce shear deformation, thereby generating viscous damping force to achieve the shock-absorbing effect. However, over time, the performance of polymer materials may be affected by environmental factors (such as temperature changes, humidity, aging, etc.), resulting in a decline in the shock-absorbing effect.

[0004] The vibration characteristics of the damping wall are key indicators for evaluating its shock-absorbing effect and safety. Traditional vibration monitoring methods for damping walls rely on manual inspections and periodic detections, lacking real-time and continuity, and unable to comprehensively monitor the dynamic characteristics of the wall. Summary of the Invention

[0005] To solve the above problems, this application proposes an intelligent monitoring method for a polymer damping wall, including:

[0006] Collect monitoring data of the target damping wall through pre-deployed sensor devices, and transmit the monitoring data to local storage in real time;

[0007] Preprocess the monitoring data, and based on the preprocessed monitoring data, analyze the vibration characteristics of the target damping wall to obtain vibration features;

[0008] Based on the vibration features, use machine learning algorithms to identify anomalies in the vibration mode of the target damping wall;

[0009] According to the identification result, determine whether there is abnormal vibration in the target damping wall.

[0010] In one example, the monitoring data includes wall vibration acceleration, wall displacement, and wall stress;

[0011] The collecting of the monitoring data of the target damping wall through pre-deployed sensor devices includes:

[0012] Collect the vibration acceleration of the wall of the target damping wall through an acceleration sensor based on a preset acquisition frequency;

[0013] Collect the wall displacement of the target damping wall through a displacement sensor based on the preset acquisition frequency;

[0014] Collect the wall stress of the target damping wall through a strain sensor based on the preset acquisition frequency.

[0015] In one example, the vibration characteristics include wall vibration intensity, spectral characteristics, natural frequency, and wall vibration mode;

[0016] Performing vibration characteristic analysis on the target damping wall based on the preprocessed monitoring data to obtain vibration characteristics, including:

[0017] Based on the preset acquisition frequency, obtain the time-domain waveform of the monitoring data, and according to the time-domain waveform, obtain the wall vibration intensity of the target damping wall;

[0018] Convert the monitoring data into a frequency-domain signal to obtain the spectrum of the target damping wall; the spectrum includes acceleration spectrum, displacement spectrum, and stress spectrum;

[0019] Based on the spectrum, perform modal analysis to identify the wall vibration mode corresponding to the natural frequency existing in the target damping wall.

[0020] In one example, the performing modal analysis based on the spectrum to identify the natural frequency and the corresponding wall vibration mode existing in the target damping wall includes:

[0021] Respectively obtain the frequency peaks existing in the acceleration spectrum and the displacement spectrum, and based on the frequency peaks, determine the natural frequency of the target damping wall;

[0022] Through a modal analysis method, according to the acceleration spectrum and the displacement spectrum, determine the wall vibration mode corresponding to the natural frequency;

[0023] According to the stress spectrum and the displacement spectrum, determine the wall stress distribution corresponding to the wall vibration mode.

[0024] In one example, the performing abnormal identification of the vibration mode of the target damping wall based on the vibration characteristics through a machine learning algorithm includes:

[0025] Call the deployed machine learning model;

[0026] Take the wall vibration intensity, the spectral characteristics, the natural frequency, and the wall vibration mode as input features and input them into the machine learning model;

[0027] Anomaly recognition of the vibration mode of the target damping wall is performed through the machine learning model.

[0028] In one example, the anomaly recognition of the vibration mode of the target damping wall through the machine learning model includes:

[0029] Through the machine learning model, it is determined whether the vibration intensity of the wall is within the normal vibration amplitude range;

[0030] Based on the normal distribution of the natural frequency, it is determined whether the natural frequency has abnormal changes;

[0031] It is determined whether the vibration mode of the wall matches the normal vibration mode;

[0032] Based on the normal spectrum distribution, it is determined whether there are abnormal changes in the spectrum characteristics.

[0033] In one example, the determination of whether there is abnormal vibration in the target damping wall according to the recognition result includes:

[0034] The prediction probability obtained from the anomaly recognition is obtained, and it is determined whether the prediction probability is higher than the probability threshold;

[0035] If so, it is determined that there is abnormal vibration in the target damping wall, an anomaly alarm is triggered, the type of abnormal vibration is identified, and a diagnostic report is output based on the type of abnormal vibration.

[0036] In one example, the identification of the type of abnormal vibration and the output of the diagnostic report based on the type of abnormal vibration include:

[0037] According to the vibration characteristics, the type of abnormal vibration is identified;

[0038] Based on the type of abnormal vibration, the corresponding maintenance suggestions are queried;

[0039] A diagnostic report is generated based on the type of abnormal vibration and the maintenance suggestions, and the diagnostic report is sent to the maintenance end.

[0040] On the other hand, the present application also proposes an intelligent monitoring device for a polymer damping wall, including:

[0041] At least one processor; and,

[0042] A memory communicatively connected to the at least one processor; wherein,

[0043] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute an intelligent monitoring method for a polymer damping wall as described in the above example.

[0044] On the other hand, the present application also proposes a non-volatile computer storage medium storing computer-executable instructions, and the computer-executable instructions are set as: an intelligent monitoring method for a polymer damping wall as described in the above example.

[0045] The intelligent monitoring method for a polymer damping wall proposed by the present application can bring the following beneficial effects:

[0046] By deploying sensor devices, the vibration data of the wall can be collected in real time and transmitted to local storage, enabling continuous monitoring of the wall vibration. The lag of manual inspection is avoided, and the monitoring system can evaluate the health status of the wall at any time and detect abnormalities in a timely manner.

[0047] By automatically collecting vibration data through pre-deployed sensors and automatically identifying abnormalities through machine learning algorithms, not only human errors are avoided, but also the standardization and accuracy of data collection are ensured. The dependence on manual intervention is greatly reduced, and the stability and reliability of the monitoring system are improved.

[0048] Real-time data transmission and anomaly recognition enable the system to predict potential structural problems in advance based on the detected changes in vibration characteristics, and can issue an alarm in a timely manner at the initial stage of an anomaly, notifying relevant personnel for inspection or repair to avoid further deterioration of the problem. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings:

[0050] Figure 1 is a schematic flowchart of an intelligent monitoring method for a polymer damping wall in an embodiment of the present application;

[0051] Figure 2 is a schematic diagram of an intelligent monitoring device for a polymer damping wall in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0053] The following will describe in detail the technical solutions provided by various embodiments of the present application in conjunction with the accompanying drawings.

[0054] As Figure 1 shown, an intelligent monitoring method for a polymer damping wall provided by an embodiment of the present application includes:

[0055] S101: Collect monitoring data of the target damping wall through pre-deployed sensor devices, and transmit the monitoring data to local storage in real time.

[0056] Specifically, various sensors such as acceleration sensors and displacement sensors are installed in advance at key positions of the target damping wall to collect vibration data of the damping wall during operation, including information such as vibration acceleration and displacement changes. The collected data is sent to a local data storage device in real time through wired or wireless transmission methods for subsequent further processing and analysis.

[0057] The sensors need to be deployed at different positions of the wall to ensure that the vibration data of the wall can be comprehensively captured. The selection of sensors is determined according to the size, design, and expected force-bearing conditions of the wall.

[0058] Among them, the monitoring data includes wall vibration acceleration, wall displacement, and wall stress, and the sensor devices include acceleration sensors, displacement sensors, and strain sensors. Through the acceleration sensor, based on a preset acquisition frequency, the wall vibration acceleration of the target damping wall is collected. Through the displacement sensor, based on a preset acquisition frequency, the wall displacement of the target damping wall is collected. Through the strain sensor, based on a preset acquisition frequency, the wall stress of the target damping wall is collected.

[0059] It should be noted that in order to ensure the accuracy and integrity of the data, the sensors need to collect high-frequency data. For polymer damping walls, the data can be sampled multiple times per second to capture the instantaneous changes in vibration.

[0060] S102: Preprocess the monitoring data, and based on the preprocessed monitoring data, analyze the vibration characteristics of the target damping wall to obtain vibration characteristics.

[0061] Specifically, after data collection, the data is obtained from local. The data needs to be preprocessed. The preprocessing stage mainly performs operations such as cleaning and filtering on the collected original monitoring data to remove interference factors such as noise and outliers in the data, making the data more accurate and reliable.

[0062] It should be noted that the preprocessing stage includes processes such as removing filtering, detrending the data, and normalizing the data. The data collected by the sensor may be affected by environmental interference (such as wind, traffic, etc.) or errors of the device itself. Therefore, noise filtering is required. Common methods include low-pass filtering, high-pass filtering, band-pass filtering, etc., which can remove unwanted high-frequency or low-frequency noise signals. Vibration data often has a certain low-frequency trend component (such as structural offset, temperature change, etc.). These parts that do not belong to the vibration characteristics need to be removed in order to better analyze the high-frequency vibration characteristics. The ranges and precisions of different sensors may vary. Therefore, it is necessary to normalize the collected data in order to unify the standards for analysis.

[0063] Furthermore, the vibration characteristics of the target damping wall are analyzed to obtain vibration characteristics. Among them, the vibration characteristics include the wall vibration intensity, frequency spectrum characteristics, natural frequency, and wall vibration mode. Based on the preset acquisition frequency, the time-domain waveform of the monitoring data is obtained. According to the time-domain waveform, the wall vibration intensity of the target damping wall is obtained. The monitoring data is converted into a frequency-domain signal to obtain the frequency spectrum of the target damping wall. The frequency spectrum includes the acceleration frequency spectrum, displacement frequency spectrum, and stress frequency spectrum. Modal analysis is performed based on the frequency spectrum to identify the wall vibration mode corresponding to the natural frequency existing in the target damping wall.

[0064] Even further, the frequency peaks existing in the acceleration frequency spectrum and the displacement frequency spectrum are respectively obtained. Based on the frequency peaks, the natural frequency of the target damping wall is determined. Through the modal analysis method, according to the acceleration frequency spectrum and the displacement frequency spectrum, the wall vibration mode corresponding to the natural frequency is determined. According to the stress frequency spectrum and the displacement frequency spectrum, the wall stress distribution corresponding to the wall vibration mode is determined.

[0065] It should be noted that the vibration characteristic analysis includes spectrum analysis, modal analysis, and amplitude analysis. Spectrum analysis includes: By performing a fast Fourier transform (FFT) on the vibration data, the vibration signal in the time domain can be converted into a frequency domain signal. This can help identify the natural frequency of the wall and judge its vibration amplitude at different frequencies. Vibrations at different frequencies represent different modes, and the wall may have a strong response at certain specific frequencies, which are the natural frequencies. By analyzing the changes in the natural frequencies, it can be speculated whether the structure has suffered damage or the damping effect has deteriorated. Modal analysis includes: Through modal analysis, the modal frequency, modal shape (the vibration form of the wall), and modal damping (reflecting the characteristics of structural energy dissipation) of the wall can be obtained. Under normal circumstances, the modal frequency and modal shape of the wall are stable, and if abnormalities occur (such as damage, aging), these characteristics will change. For example, if the amplitude at a certain frequency is too large, it may indicate that the damping wall function at that frequency has deteriorated. Amplitude analysis includes: The vibration amplitude (or acceleration, displacement amplitude) is a direct indicator of the damping effect of the wall. Usually, the amplitude of the wall should be maintained within a set range, and an excessive amplitude means insufficient damping effect. By comparing the amplitudes under different working conditions, the response changes of the wall under different loads or environmental conditions can be understood. If the amplitude shows an abnormal increase, it may mean that the damping performance of the wall has decreased.

[0066] S103: Based on the vibration characteristics, an abnormal identification of the vibration mode of the target damping wall is performed through a machine learning algorithm.

[0067] Specifically, a large amount of vibration characteristic data collected under normal conditions before is used to train a machine learning model, such as a support vector machine, a random forest, a neural network, etc. In the embodiment of the present application, a random forest network is used, and the trained model can learn the characteristic rules of the normal vibration mode. Through the trained machine learning model, an abnormal identification of the vibration mode of the target damping wall is performed.

[0068] Specifically, the wall vibration intensity, spectrum characteristics, natural frequency, and wall vibration mode are used as input features and input into the machine learning model. Through the machine learning model, an abnormal identification of the vibration mode of the target damping wall is performed. Through the machine learning model, it is judged whether the wall vibration intensity is within the normal vibration amplitude range, and based on the normal distribution of the natural frequency, it is judged whether the natural frequency has an abnormal change, and it is judged whether the wall vibration mode matches the normal vibration mode, and based on the normal spectrum distribution, it is judged whether there is an abnormal change in the spectrum characteristics.

[0069] It should be noted that the wall vibration intensity is used to determine whether the wall has received a large external force or has damage. Abnormal vibrations are often accompanied by a large intensity. The natural frequency is used to determine whether there are cracks or corrosion in the wall. When damage (such as cracks or corrosion) occurs, it usually causes a shift or change in the natural frequency. The spectral characteristics can provide the vibration energy distribution of the wall at different frequencies. An abnormal spectrum may indicate local damage or non-uniform vibration response in the wall. Changes in the wall vibration mode may indicate damage or instability in the wall. When there are obvious changes in the mode or abnormal energy concentration, abnormal vibrations are likely to occur. By extracting the vibration modes of each mode and their energy distribution, it can help the model understand the health status of the wall.

[0070] S104: According to the recognition result, determine whether there is abnormal vibration in the target damping wall.

[0071] Specifically, when the machine learning model recognizes that the current vibration mode is abnormal, then it can be determined that there is abnormal vibration in the target damping wall. Further evaluate the abnormal situation to judge the severity of the abnormality, possible causes, etc., so as to take corresponding measures in time, such as maintenance, adjustment, etc.

[0072] Specifically, obtain the prediction probability obtained by abnormal recognition, judge whether the prediction probability is higher than the probability threshold. If so, determine that there is abnormal vibration in the target damping wall, trigger an abnormal alarm, and identify the type of abnormal vibration. Based on the type of abnormal vibration, output a diagnostic report. According to the vibration characteristics, identify the type of abnormal vibration. Based on the type of abnormal vibration, query the corresponding maintenance suggestions. Generate a diagnostic report based on the type of abnormal vibration and the maintenance suggestions, and send the diagnostic report to the maintenance end.

[0073] Among them, the probability threshold is set according to the design parameters of the wall and historical monitoring data. When the prediction probability is higher than the probability threshold, an alarm is automatically issued. The diagnostic report includes the change trend of vibration characteristics, the analysis of potential faults, the evaluation of shock absorption effect, and suggestions for future maintenance, helping engineers understand the health status of the wall.

[0074] In the embodiment of the present application, by deploying sensor devices to collect the vibration data of the wall in real time and transmit it to local storage, continuous monitoring of the wall vibration can be achieved. It avoids the lag of manual inspection, enabling the monitoring system to evaluate the health status of the wall at any time and detect abnormalities in time.

[0075] By automatically collecting vibration data through pre-deployed sensors and automatically performing abnormal recognition through machine learning algorithms, it not only avoids human errors but also ensures the standardization and accuracy of data collection. Greatly reduces the dependence on manual intervention and improves the stability and reliability of the monitoring system.

[0076] Real-time data transmission and anomaly recognition enable the system to give early warnings about potential structural problems based on detected changes in vibration characteristics, be able to issue alarms in a timely manner at the initial stage of anomalies, notify relevant personnel for inspections or repairs, and prevent problems from deteriorating further.

[0077] As Figure 2 shown, the embodiment of the present application also proposes an intelligent monitoring device for a polymer damping wall, including:

[0078] At least one processor; and,

[0079] A memory communicatively connected to the at least one processor; wherein,

[0080] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute: an intelligent monitoring method for a polymer damping wall as described in any one of the above embodiments.

[0081] The embodiment of the present application also provides a non-volatile computer storage medium storing computer-executable instructions, and the computer-executable instructions are set as: an intelligent monitoring method for a polymer damping wall as described in any one of the above embodiments.

[0082] The various embodiments in the present application are all described in a progressive manner. For the same or similar parts among the various embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for the relevant parts, reference can be made to the partial description of the method embodiments.

[0083] The devices and media provided in the embodiments of the present application correspond one by one to the methods. Therefore, the devices and media also have beneficial technical effects similar to those of their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be elaborated here.

[0084] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0085] The present invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowchart illustrations and / or block diagrams, and combinations of flows and / or blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart Figure 1 for one or more flows and / or blocks Figure 1 of the block or blocks.

[0086] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the functions specified in the flowchart Figure 1 for one or more flows and / or blocks Figure 1 of the block or blocks.

[0087] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart Figure 1 for one or more flows and / or blocks Figure 1 of the block or blocks.

[0088] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0089] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0090] A computer-readable medium includes both permanent and non-permanent, removable and non-removable media and can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0091] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising the element.

[0092] The above description is only for the embodiments of the present application and is not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. An intelligent monitoring method for a polymer damping wall, characterized in that: include: Collect monitoring data of the target damping wall through pre-deployed sensor equipment, and transmit the monitoring data to local storage in real time; Preprocessing the monitoring data, and based on the preprocessed monitoring data, performing vibration characteristic analysis on the target damping wall to obtain vibration characteristics; Based on the vibration characteristics, anomaly identification is performed on the vibration mode of the target damping wall through a machine learning algorithm; According to the recognition result, it is determined whether the target damping wall has abnormal vibration.

2. The intelligent monitoring method of a polymer damping wall according to claim 1 is characterized in that: The monitoring data include wall vibration acceleration, wall displacement and wall stress; The monitoring data of the target damping wall is collected by using the pre-deployed sensor equipment, specifically including: Using an acceleration sensor, based on a preset acquisition frequency, the wall vibration acceleration of the target damping wall is collected; By means of a displacement sensor, based on the preset acquisition frequency, the wall displacement of the target damping wall is acquired; The wall stress of the target damping wall is collected by a strain sensor based on the preset collection frequency.

3. The intelligent monitoring method of a polymer damping wall according to claim 2 is characterized in that: The vibration characteristics include wall vibration intensity, frequency spectrum characteristics, natural frequency and wall vibration mode; The vibration characteristics of the target damping wall are analyzed based on the pre-processed monitoring data to obtain vibration characteristics, which specifically includes: Based on the preset acquisition frequency, a time domain waveform of the monitoring data is acquired, and according to the time domain waveform, a wall vibration intensity of the target damping wall is obtained; Converting the monitoring data into frequency domain signals to obtain the frequency spectrum of the target damping wall; the frequency spectrum includes acceleration spectrum, displacement spectrum and stress spectrum; Modal analysis is performed based on the frequency spectrum to identify the wall vibration mode corresponding to the natural frequency of the target damping wall.

4. The intelligent monitoring method of a polymer damping wall according to claim 3 is characterized in that: The performing modal analysis based on the frequency spectrum to identify the natural frequency of the target damping wall and the corresponding wall vibration mode specifically includes: Respectively acquiring frequency peaks existing in the acceleration spectrum and the displacement spectrum, and determining the natural frequency of the target damping wall based on the frequency peaks; Determine the wall vibration mode corresponding to the natural frequency according to the acceleration spectrum and the displacement spectrum by a modal analysis method; The wall stress distribution corresponding to the wall vibration mode is determined according to the stress spectrum and the displacement spectrum.

5. The intelligent monitoring method of a polymer damping wall according to claim 3 is characterized in that: Based on the vibration characteristics, the vibration mode of the target damping wall is identified as abnormal by a machine learning algorithm, specifically including: Call the deployed machine learning model; Input the wall vibration intensity, the frequency spectrum feature, the natural frequency and the wall vibration mode as input features into the machine learning model; The vibration mode of the target damping wall is abnormally identified through the machine learning model.

6. The intelligent monitoring method of a polymer damping wall according to claim 5, characterized in that: The abnormal identification of the vibration mode of the target damping wall by using the machine learning model specifically includes: By using the machine learning model, determining whether the wall vibration intensity is within a normal vibration amplitude range; and judging whether the natural frequency has undergone abnormal changes based on the normal distribution of the natural frequency; and determining whether the wall vibration mode matches the normal vibration mode; And based on the normal spectrum distribution, it is determined whether there is any abnormal change in the spectrum characteristics.

7. The intelligent monitoring method of a polymer damping wall according to claim 1, characterized in that: The determining, according to the recognition result, whether the target damping wall has abnormal vibration specifically includes: Obtaining the predicted probability obtained by abnormality identification, and determining whether the predicted probability is higher than a probability threshold; If so, it is determined that the target damping wall has abnormal vibration, an abnormal alarm is triggered, and the type of abnormal vibration is identified, and a diagnosis report is output based on the abnormal vibration type.

8. The intelligent monitoring method of a polymer damping wall according to claim 7, characterized in that: The identifying the abnormal vibration type and outputting a diagnosis report based on the abnormal vibration type specifically includes: identifying the abnormal vibration type according to the vibration characteristics; Based on the abnormal vibration type, query corresponding maintenance suggestions; A diagnosis report is generated based on the abnormal vibration type and the maintenance suggestion, and the diagnosis report is sent to a maintenance end.

9. An intelligent monitoring device for polymer damping wall, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute an intelligent monitoring method for a polymer damping wall as described in any one of claims 1 to 8.

10. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions are configured as: an intelligent monitoring method for a polymer damping wall as described in any one of claims 1 to 8.

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